A statistical process control framework is proposed to monitor non-linear profiles. The proposed methodology aims at identifying mean shifts or ‘shape changes’ in a profile. Discrete wavelet transformation (DWT) is applied to separate variation or noise from profile contours. B-splines are adopted to generate critical points to define the shape of a profile. The proposed method is innovative in that users can divide a profile into multiple segments to be monitored simultaneously. The high dimensionality problem that hinders the implementation of multivariate control charts can be solved by this framework. The distance difference statistic for each segment provides diagnostic information when the process of interest is out of control. These proposed statistics form a vector to be fed into any multivariate control chart such as the Hotelling T ² control chart. A decomposition method can also be applied on the T ² statistics when an out-of-control profile is detected. A simulation study applied to a forging process is conducted to demonstrate the property of the proposed method. The proposed method is capable of detecting profile shifts and identifying the exact location of problematic segments.

We propose a neural fuzzy (NF) control chart for identifying process mean shifts. A supervised multi-layer backpropagation neural network is trained off-line to detect various mean shifts in a production process. In identifying mean shifts in real-time usage, the neural network's outputs are classified into various decision regions using a fuzzy set scheme. The approach offers better performance and additional advantages over conventional control charts. Simulation results show that the proposed NF control charts are superior to conventional X-bar charts and CUSUM charts in terms of the average run lengths (ARL). The proposed system also has the ability to identify the magnitude of a mean shift, in addition to the Shewhart-type control chart heuristic rules. Correct classification percentages are studied. Furthermore, general guidelines are given for the proper use of the proposed NF charts.